Generating snippets based on content features

ABSTRACT

Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate generation of snippets. In embodiments, text features within a keyword-sentence window are identified. The text features are utilized to determine break features that indicate favorability of breaking at a particular location of the keyword-sentence window. The break features are used to recognize features of partial snippets such that a snippet score to indicate the strength of the partial snippet can be calculated. Snippet scores associated with partial snippets are compared to select an optimal snippet, that is, the snippet having the highest snippet score.

BACKGROUND

In response to user queries, search results are oftentimes presented inthe form of captions including a title, a URL, and a snippet. A snippetsummarizes or characterizes a corresponding webpage and generallyincludes query terms input by the user. In this regard, snippets areusually a selection of text from the corresponding webpage that includekeywords that match query terms of the user's query. The context thatsurrounds those keywords, however, is oftentimes truncated to maintain apredetermined snippet length. Such snippet truncations can occur atseemingly arbitrary boundaries resulting in an omission of words deemedvaluable by providing context, completeness, and/or coherency. In thisregard, arbitrary snippet boundaries can result in reduced readabilityand understandability thereby making it more difficult for a user todetermine the relevance or content of a document associated with asearch result. Accordingly, a user may overlook a search result orunnecessarily select a search result to further view contents thereof.

Generating snippets in accordance with optimal or preferred snippetboundaries provides fewer inscrutable snippets containing abrupttruncations of context. Such snippets having optimal snippet boundariescan initially provide users with higher quality information in responseto a user query. As such, users can more accurately determine whether toclick through to a document corresponding with a search result. By wayof example only, assume that essential information a user is seeking,such as a “punch line” or an “answer,” is at the end of a sentencehaving keywords that match query terms. In conventional snippetconstruction based primarily on length, however, the end of a sentenceis oftentimes truncated to accommodate such length restrictions. On theother hand, a snippet boundary that occurs at the end of the sentenceprovides the essential information the user is seeking.

SUMMARY

Embodiments of the present invention relate to systems, methods, andcomputer-readable media for, among other things, facilitating generationof snippets based on content features. In this regard, embodiments ofthe present invention facilitate snippet generation to enhance thesnippet content provided to a user. Accordingly, a snippet havingsnippet boundaries that align with natural breaks in the text and thatavoid omitting potentially significant content enable a user to betterunderstand and comprehend content in association with a search result.Embodiments of the invention utilize features that describe orcharacterize text as well as features that indicate favorability ofsnippet boundaries to identify and/or select an optimal snippet forpresenting in association with a search result.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a block diagram of an exemplary computing environment suitablefor use in implementing embodiments of the present invention;

FIG. 2 is a block diagram of an exemplary computing system architecturesuitable for use in implementing embodiments of the present invention;

FIG. 3 is a block diagram of an exemplary computer system for use inimplementing embodiments of the present invention;

FIG. 4A is a flow diagram showing a method for facilitating generationof snippets, in accordance with an embodiment of the present invention;and

FIG. 4B is a continuation of the flow diagram of FIG. 4A showing amethod for facilitating generation of snippets, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

Embodiments of the present invention relate to systems, methods, andcomputer storage media having computer-executable instructions embodiedthereon that facilitate generation of snippets. In this regard,embodiments of the present invention facilitate identifying andselecting an optimal snippet(s) to present or display in associationwith a search result (e.g., a webpage search result). An optimalsnippet, as used herein, refers to a snippet (i.e., a portion) ofcontent intended to provide a user with desirable, appropriate, orsignificant information. That is, an optimal snippet results in anunderstandable and a comprehendible snippet. Accordingly, a user viewingsnippets in association with search results is provided with higherquality information in response to a query and can thereby more easilyand accurately determine whether to click through to a search result.

To provide such an optimal snippet, the snippet conforms with optimalsnippet boundaries. A snippet boundary, as used herein, refers to alocation at which webpage content (e.g., a keyword-sentence window) is,or is to be, truncated to generate a snippet. An optimal or preferredsnippet boundary refers to a snippet boundary that results in areadable, understandable, and comprehendible snippet.

Accordingly, in one aspect, the present invention is directed to one ormore computer storage media having computer-executable instructionsembodied thereon, that when executed, cause a computing device toperform a method for facilitating generation of snippets provided inassociation with search results. The method includes referencing akeyword-sentence window comprising a sequence of tokens includingkeywords that match query terms. The method also includes identifying apart-of-speech for tokens. The method further includes utilizing thepart-of-speech corresponding with each of the tokens to identify textfeatures associated with a span including two or more tokens. The textfeatures being used to generate a snippet comprising a portion of thekeyword-sentence window truncated at optimal snippet boundaries.

In another aspect, the present invention is directed to a method forfacilitating generation of snippets provided in association with searchresults. The method includes identifying features for spans within akeyword-sentence window. At least a portion of the text features areidentified based on a part-of-speech identifier associated with tokensof the span. Break features associated with the spans are determinedusing the text features. The break features provide an indication ofwhether a snippet boundary is favorable relative to a particularposition within the keyword-sentence window. The break features areutilized to generate a snippet comprising a portion of thekeyword-sentence window truncated at appropriate snippet boundaries.

In yet another aspect, the present invention is directed to one or morecomputer storage media having computer-executable instructions embodiedthereon, that when executed, cause a computing device to perform amethod for facilitating generation of snippets provided in associationwith search results. The method includes identifying text featuresassociated with spans within a keyword-sentence window. Thekeyword-sentence window includes at least one keyword that matches atleast one query term. At least a portion of the text features areidentified based on a part-of-speech identifier associated with tokensof the span. Break features associated with the spans are determinedusing the one or more text features. The break features provide anindication of whether a snippet boundary is favorable relative to aparticular position within the keyword-sentence window. Partial snippetscomprising portions of the keyword-sentence window are generated.Snippet features are identified for each partial snippet, and a score isdetermined for each of the partial snippets that indicates favorabilityof truncating the keyword-sentence window at snippet boundaries asindicated in the partial snippet. Based on the scores, a partial snippetis selected to display in association with a search result. The selectedpartial snippet is designated as having optimal snippet boundaries.

Having briefly described an overview of the present invention, anexemplary operating environment in which various aspects of the presentinvention may be implemented is described below in order to provide ageneral context for various aspects of the present invention. Referringto the drawings in general, and initially to FIG. 1 in particular, anexemplary operating environment for implementing embodiments of thepresent invention is shown and designated generally as computing device100. Computing device 100 is but one example of a suitable computingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the invention. Neither should thecomputing device 100 be interpreted as having any dependency orrequirement relating to any one or combination of componentsillustrated.

Embodiments of the invention may be described in the general context ofcomputer code or machine-useable instructions, includingcomputer-executable instructions such as program modules, being executedby a computer or other machine, such as a personal data assistant orother handheld device. Generally, program modules including routines,programs, objects, components, data structures, etc., refer to code thatperform particular tasks or implement particular abstract data types.Embodiments of the invention may be practiced in a variety of systemconfigurations, including hand-held devices, consumer electronics,general-purpose computers, more specialty computing devices, etc.Embodiments of the invention may also be practiced in distributedcomputing environments where tasks are performed by remote-processingdevices that are linked through a communications network.

With reference to FIG. 1, computing device 100 includes a bus 110 thatdirectly or indirectly couples the following devices: memory 112, one ormore processors 114, one or more presentation components 116,input/output ports 118, input/output components 120, and an illustrativepower supply 122. Bus 110 represents what may be one or more busses(such as an address bus, data bus, or combination thereof). Although thevarious blocks of FIG. 1 are shown with lines for the sake of clarity,in reality, delineating various components is not so clear, andmetaphorically, the lines would more accurately be grey and fuzzy. Forexample, one may consider a presentation component such as a displaydevice to be an I/O component. Additionally, many processors havememory. The inventors hereof recognize that such is the nature of theart, and reiterate that the diagram of FIG. 1 is merely illustrative ofan exemplary computing device that can be used in connection with one ormore embodiments of the present invention. Distinction is not madebetween such categories as “workstation,” “server,” “laptop,” “hand-helddevice,” etc., as all are contemplated within the scope of FIG. 1 andreference to “computing device.”

Computing device 100 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 100 and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable media may comprise computerstorage media and communication media. Computer storage media includesvolatile and nonvolatile, removable and non-removable media implementedin any method or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computing device 100. Communication mediatypically embodies computer-readable instructions, data structures,program modules or other data in a modulated data signal such as acarrier wave or other transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

Memory 112 includes computer-storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, nonremovable, ora combination thereof. Exemplary hardware devices include solid-statememory, hard drives, optical-disc drives, etc. Computing device 100includes one or more processors that read data from various entitiessuch as memory 112 or I/O components 120. Presentation component(s) 116present data indications to a user or other device. Exemplarypresentation components include a display device, speaker, printingcomponent, vibrating component, etc.

I/O ports 118 allow computing device 100 to be logically coupled toother devices including I/O components 120, some of which may be builtin. Illustrative components include a microphone, joystick, game pad,satellite dish, scanner, printer, wireless device, etc.

With reference to FIG. 2, a block diagram is illustrated that shows anexemplary computing system architecture 200 configured for use inimplementing embodiments of the present invention. It will be understoodand appreciated by those of ordinary skill in the art that the computingsystem architecture 200 shown in FIG. 2 is merely an example of onesuitable computing system and is not intended to suggest any limitationas to the scope of use or functionality of the present invention.Neither should the computing system architecture 200 be interpreted ashaving any dependency or requirement related to any singlemodule/component or combination of modules/components illustratedtherein.

Computing system architecture 200 includes a server 202, a storagedevice 204, and an end-user device 206, all in communication with oneanother via a network 208. The network 208 may include, withoutlimitation, one or more local area networks (LANs) and/or wide areanetworks (WANs). Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets and the Internet.Accordingly, the network 208 is not further described herein.

The storage device 204 is configured to store information associatedwith snippets. In various embodiments, such information may include,without limitation, webpage content, keyword-sentence windows, snippets,partial snippets, tokens, spans, text features, break features, snippetfeatures, and/or the like. In embodiments, the storage device 204 isconfigured to be searchable for one or more of the items stored inassociation therewith. It will be understood and appreciated by those ofordinary skill in the art that the information stored in associationwith the storage device 204 may be configurable and may include anyinformation relevant to one or more webpage content, keyword-sentencewindows, snippets, partial snippets, tokens, spans, text features, breakfeatures, snippet features, and/or the like. The content and volume ofsuch information are not intended to limit the scope of embodiments ofthe present invention in any way. Further, though illustrated as asingle, independent component, the storage device 204 may, in fact, be aplurality of storage devices, for instance a database cluster, portionsof which may reside on the server 202, the end-user device 206, anotherexternal computing device (not shown), and/or any combination thereof.

Each of the server 202 and the end-user device 206 shown in FIG. 2 maybe any type of computing device, such as, for example, computing device100 described above with reference to FIG. 1. By way of example only andnot limitation, each of the server 202 and the end-user device 206 maybe a personal computer, desktop computer, laptop computer, handhelddevice, mobile handset, consumer electronic device, or the like. Itshould be noted, however, that embodiments are not limited toimplementation on such computing devices, but may be implemented on anyof a variety of different types of computing devices within the scope ofembodiments hereof.

The server 202 may include any type of application server, databaseserver, or file server configurable to perform the methods describedherein. In addition, the server 202 may be a dedicated or shared server.One example, without limitation, of a server that is configurable tooperate as the server 202 is a structured query language (“SQL”) serverexecuting server software such as SQL Server 2005, which was developedby the Microsoft® Corporation headquartered in Redmond, Wash.

Components of server 202 (not shown for clarity) may include, withoutlimitation, a processing unit, internal system memory, and a suitablesystem bus for coupling various system components, including one or moredatabases for storing information (e.g., files and metadata associatedtherewith). Each server typically includes, or has access to, a varietyof computer-readable media. By way of example, and not limitation,computer-readable media may include computer-storage media andcommunication media. In general, communication media enables each serverto exchange data via a network, e.g., network 208. More specifically,communication media may embody computer-readable instructions, datastructures, program modules, or other data in a modulated data signal,such as a carrier wave or other transport mechanism, and may include anyinformation-delivery media. As used herein, the term “modulated datasignal” refers to a signal that has one or more of its attributes set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, communication media includes wired mediasuch as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared, and other wireless media. Combinationsof any of the above also may be included within the scope ofcomputer-readable media.

It will be understood by those of ordinary skill in the art thatcomputing system architecture 200 is merely exemplary. While the server202 is illustrated as a single unit, one skilled in the art willappreciate that the server 202 is scalable. For example, the server 202may in actuality include a plurality of servers in communication withone another. Moreover, the storage device 204 may be included within theserver 202 or end-user device 206 as a computer-storage medium. Thesingle unit depictions are meant for clarity, not to limit the scope ofembodiments in any form.

As shown in FIG. 2, the end-user device 206 includes a user input module210 and a presentation module 212. In some embodiments, one or both ofthe modules 210 and 212 may be implemented as stand-alone applications.In other embodiments, one or both of the modules 210 and 212 may beintegrated directly into the operating system of the end-user device206. It will be understood by those of ordinary skill in the art thatthe modules 210 and 212 illustrated in FIG. 2 are exemplary in natureand in number and should not be construed as limiting. Any number ofmodules may be employed to achieve the desired functionality within thescope of embodiments hereof.

The user input module 210 is configured for receiving input. Such inputmight include, for example, user search queries. Typically, input isinput via a user interface (not shown) associated with the end-userdevice 206, or the like. Upon receiving input, the presentation module212 of the end-user device 206 is configured for presenting snippets,for example, in association with search results. Embodiments are notintended to be limited to visual display but rather may also includeaudio presentation, combined audio/video presentation, and the like.

FIG. 3 illustrates an exemplary computing system 300 for facilitatinggeneration of snippets. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, and groupings of functions, etc.) can be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed by oneor more entities may be carried out be hardware, firmware, and/orsoftware. For instance, various functions may be carried out by aprocessor executing instructions stored in memory.

As shown in FIG. 3, the computing system 300 includes, among othercomponents, includes a keyword-sentence builder 310, a featureidentifier 312, and a snippet generator 314. In some embodiments, one ormore of the illustrated components/modules may be implemented asstand-alone applications. In other embodiments, one or more of theillustrated components/modules may be integrated directly into theoperating system of the server 202, a cluster of servers (not shown)and/or the end-user device 206. It will be understood by those ofordinary skill in the art that the components/modules illustrated inFIG. 3 are exemplary in nature and in number and should not be construedas limiting. Any number of components/modules may be employed to achievethe desired functionality within the scope of embodiments hereof.Further, components/modules may be located on any number of servers orcomputing devices.

The keyword-sentence builder 310 is configured to generatekeyword-sentence windows, as described more fully below. A keywordsentence, as used herein, refers to a sentence having one or morekeywords that correspond with query terms of a query. In this regard,upon receiving a query having query terms, a document (e.g., a webpage)containing keywords corresponding with or matching the query termsincludes one or more keyword sentences. A document can be, for example,a webpage or website relevant to a query. A keyword-sentence window, asused herein, refers to at least one or more keyword sentences, or aportion thereof, including one or more keywords. In this regard, akeyword-sentence window can include document content (e.g., sentences)in addition to a keyword sentence(s) having a keyword(s). For example, akeyword-sentence window might include a couple of sentences before akeyword-sentence and/or a couple of sentences following a keywordsentence. Further, in some embodiments, a keyword-sentence windowincludes a portion of a keyword sentence and/or a portion of otherdocument content (e.g., sentences). As can be appreciated, a particularkeyword sentence, or portion thereof, can be associated with multiplekeyword-sentence windows. For instance, one keyword-sentence windowmight include a keyword sentence and two previous sentences whileanother keyword-sentence window might include the same keyword sentenceand two sentences following the keyword sentence.

In embodiments, the keyword-sentence builder 310 includes a sentencereferencing component 316, a keyword-set referencing component 318, awindow generating component 320, and keyword-sentence modifyingcomponent 322. The sentence referencing component 316 is configured toreference a set of one or more sentences, or portions thereof, of adocument. In this regard, one or more sentences, or portions thereof,can be identified, determined, extracted, recognized, accessed,received, retrieved, etc. In one embodiment, sentences are referencedfrom a sentence breaker, or other component. A sentence breaker, as usedherein, identifies sentences by recognizing a beginning point and anending point of a sentence. A sentence breaker might identify allsentences within a document or a portion of sentences within a document.By way of example only, assume that a particular document is identifiedas relevant to a query input by a user. In such a case, a sentencebreaker might reference the document and identify or specify eachsentence within the document. Alternatively, upon referencing adocument, a sentence breaker might identify or specify a portion ofsentences within a document (e.g., sentences at the top portion of thedocument, sentences having keywords, sentences surrounding keywordsentences, etc.). A sentence can be designated or specified as such inany manner such as, for example, an indication of a beginning point ofeach sentence and/or an indication of an ending point of each sentence.

The keyword-set referencing component 318 is configured to reference oneor more keyword sets. In this regard, one or more keyword sets can beidentified, determined, extracted, recognized, accessed, received,retrieved, etc. A keyword set, as used herein, refers to one or morekeywords within a document that correspond or match query terms of aquery. In some embodiments, a keyword set might exist for eachcombination of keywords. Alternatively, a predetermined number orarrangement of keyword sets might exist. That is, a select group of oneor more keyword sets might be generated (e.g., via the keyword-setreferencing component 318 or other component such as a keyword-setgenerator) and/or referenced. In such a case, each keyword within adocument that matches a query term might be recognized while a portionof such keywords are selected as a keyword set.

By way of example only, assume that a query includes query terms A, B,and C. In such a case, one keyword set might be A1, B1, and C1, in which“1” indicates a particular instance (e.g., first instance) of thekeyword within the document. Another keyword set might be A1, B2, C1, inwhich “1” indicates a particular instance of the keyword and “2”indicates another instance (e.g., a second instance) of the keyword.That is, B1 and B2 are both occurrences of a particular keyword matchinga query term, such as “dog,” but refer to different instances orlocations of the keyword “dog” within the document. As can beappreciated, a keyword set can be a portion of query terms of a query.For example, assume again that a query includes query terms A, B, and C.In such a case, a keyword set might be “A1,” while another keyword setmight be “B2.”

The window generating component 320 is configured to generatekeyword-sentence windows. In embodiments, the window generatingcomponent 320 utilizes one or more keyword sets, such as keyword setsreferenced by the keyword-set referencing component 318, and one or moredocument sentences, such as sentences referenced by the sentencereferencing component 316, to generate keyword-sentence windows. In somecases, the window generating component 320 generates one or morekeyword-sentence windows for a keyword set. In this regard, for aparticular keyword set, a range of one or more sentences, or portionsthereof, surrounding the keywords of the keyword set is identified anddesignated as a keyword-sentence window for the keyword set.Accordingly, the keyword-sentence window captures each keyword of thekeyword set. Additionally or alternatively, the window generatingcomponent 320 generates one or more keyword-sentence windows for eachkeyword of a keyword set. For example, assume that a keyword setcomprises three keywords. For each keyword, a range of one or moresentences, or portions thereof, that surround the keyword is identifiedand designated as a keyword-sentence window for the keyword or keywordset. In other words, the keyword set having three keywords might beassociated with three separate keyword-sentence windows. As such, thekeyword-sentence window captures a portion of keywords of a keyword set.

The keyword-sentence modifying component 322 is configured to modifykeyword-sentence windows, if necessary. In such a case, keyword-sentencewindows might be merged together if two or more keyword-sentence windowsare adjacent to one another or overlap with one another. In this regard,the keyword-sentence modifying component 322 identifies whetherkeyword-sentence windows overlap or are sufficiently proximate that asingle keyword-sentence window should be utilized. If so, multiplekeyword-sentence windows can be aggregated or otherwise modified, forexample, by removing or deleting a keyword-sentence window.

By way of example only, assume that a query input by a user is “WrightIncontainables” thereby having two query terms “Wright” and“Incontainables.” Further assume that upon receiving the query, adocument recognized as relevant to the query includes the followingtext: “Susan Wright (born 1948) writes science fiction novels, and livesin San Francisco, Calif. She has written two best sellers, ‘The GreenGlass’ and Incontainables.'” As such, a keyword set might be [Wright,Incontainables]. In some cases, the position or instance that thekeyword appears in the document is identified in association with thekeyword set to specify the particular instance of the keyword within thedocument. Initially, a keyword-sentence window in association with thekeyword “Wright” might be or include “Susan Wright (born 1948) writesscience fiction novels, and lives in San Francisco, Calif.” Similarly, akeyword-sentence window in association with the keyword “Incontainables”might be or include “She has written two best sellers, “The Green Glass”and “Incontainables.” Although keyword-sentence windows can includemultiple document sentences on either side of each keyword, only onesentence is selected here to simplify the example. Because the twokeyword-sentence windows are adjacent to one another within thedocument, the keyword-sentence modifying component 322 might aggregatethe two windows into a single keyword-sentence window.

The feature identifier 312 is configured to identify content features inassociation with the keyword-sentence windows. Content features, as usedherein, refer to features that describe content, such as tokens, spansand breaks therebetween, of a keyword-sentence window. In embodiments,the feature identifier 312 includes a tokenizing component 330, atext-feature indentifying component 332, and a break-feature identifyingcomponent 334.

The tokenizing component 330 is configured to generate or identify a setof tokens in association with a keyword-sentence window. A token, asused herein, refers to a distinct portion of a sentence. A token can be,for example, a word, a punctuation mark (e.g., ., ,, ;, “, (,), !, ?,etc.), an acronym, or the like. Accordingly, the tokenizing component330 references a keyword-sentence window for which a list of tokens isgenerated or identified. As can be appreciated, one or morekeyword-sentence windows can be received, retrieved, identified,determined, recognized, accessed, or the like.

By way of example only, assume that a referenced keyword-sentence windowis “Susan Wright (born 1948) writes science fiction novels, and lives inSan Francisco, Calif.” In such a case, the resulting tokens can include:“Susan”, “Wright”, “(“, “born”, “1948”,”)”, “writes”, “science”,“fiction”, “novels”, “,”, “and”, “lives”, “in”, “San”, “Francisco”, “,”,“California”, “.”. Although illustrated as identifying tokens inassociation with each word, punctuation mark, etc. of thekeyword-sentence window, a set of tokens identified for akeyword-sentence window can correspond with a portion of the wordsand/or punctuation marks of the keyword-sentence window.

The text-feature identifying component 332 is configured to identifytext features. A text feature, as used herein, refers to a feature thatdescribes a token or a span of text within a keyword-sentence window. Aspan refers to a set of two or more consecutive tokens. A text featuremay be, without limitation, a token part-of-speech, a bigram type, anamed entity, a breakpoint, an address, a phone number, and a predefinedname. A token part-of-speech feature refers to an indication of apart-of-speech of a particular token. A part-of-speech includes, forexample, a noun, a verb, a conjunction, a preposition, an adjective, anadverb, an interjection, etc. A part-of-speech feature can be identifiedand/or tagged or annotated by a parts-of-speech tagger (POS tagger). Assuch, a POS tagger can mark, tag, label, or annotate tokens withparts-of-speech (POS) identifiers. Sample POS identifiers include, butare not limited to, VBN (verb past participle), DT (determinant), NNP(proper noun singular), NN (noun singular or mass), SYM (symbol), CD(cardinal number), CONJ (conjunction), PRP (personal pronoun), etc.Although POS identifiers are generally described using acronyms, POSidentifiers can be any identifier capable of identifying apart-of-speech.

By way of example only, assume that a referenced keyword-sentence windowis “Susan Wright (born 1948) writes science fiction novels, and lives inSan Francisco, Calif.” In such a case, the resulting tokens can include:“Susan”, “Wright”, “(“, “born”, “1948”,”)”, “writes”, “science”,“fiction”, “novels”, “,”, “and”, “lives”, “in”, “San”, “Francisco”, “,”,“California”, “.”. Accordingly, token part-of-speech features inassociation with the keyword-sentence window might be: [Susan/NNP,Wright/NNP, (/(, born/VBD, 1948/CD,)/), writes/VBZ, science/NN,fiction/NN, novels/NNS ,/, and/CONJ, lives/VBZ, in/PRP, San/NNP,Francisco/NNP, ,/,, California/NNP, ./.]. As can be appreciated, in somecases, a token POS feature refers to the POS identifier (e.g., NNP, VBZ,etc.). Alternatively, a token POS feature might refer to the combinationof the token and the POS identifier associated therewith (e.g.,Susan/NNP).

In embodiments, part-of-speech features associated with tokens are usedto recognize other text features of tokens, or spans in associationtherewith. In this regard, a POS identifier that identifies apart-of-speech of a token can be used to identify other text features ofthe token or a span including the token. For instance, a part-of-speechfeature can be used to identify a bigram type feature, a named entityfeature, and a breakpoint feature. A bigram type feature refers to anindication of a type of bigram. A bigram, as used herein, is a sequenceof two consecutive tokens. A bigram type provides a sequence of twoparts-of-speech identifiers that correspond with a sequence of twoconsecutive tokens (i.e., a bigram).

By way of example only, assume that a sequence of two consecutive tokensis “science” and “fiction.” Further assume that “science” is identifiedas a noun (i.e., NN) and that “fiction” is also identified as a noun(i.e., NN). In such a case, the bigram type is NN_NN. Such a feature caninclude, for example, the bigram and bigram type (e.g.,[science_fiction, Bigram_NN_NN]). Alternatively, a bigram type featurerefers to the indication of the bigram type (e.g., NN_NN). In somecases, a bigram type may be identified and/or annotated for each bigramor each bigram containing two words. In other cases, a bigram type mightbe identified and/or annotated for a portion of bigrams within akeyword-sentence window. For example, only bigrams that match at leastone of a predefined bigram type might be identified and/or annotated asa text feature.

Similarly, a named entity feature can also be recognized usingparts-of-speech identifiers. In such a case, a named entity can beidentified as such when a span or token is associated with a particularsequence of part-of-speech identifiers (e.g., sequence of proper noun(NNP)) or a particular POS identifier. For instance, in a case where twoor more consecutive words comprise a sequence of NNPs, a named entitycan be identified and annotated as such. Stated differently, in thisexample, a named entity feature is recognized when a bigram type isidentified as NNP_NNP. Accordingly, a name feature can indicate a nameof a human or any other entity comprising a sequence of NNPs (i.e., aspan of proper nouns). Named entities are generally associated with acategory, such as, for example, names of persons, organizations,locations, expressions of times, quantities, monetary values,percentages, etc. Accordingly, in accordance with identifying that atoken or span is a named entity, the category to which the entitybelongs can also be recognized. As such, a named entity feature caninclude the token or span, an indication that the token or span is anamed entity, an indication of the category to which the token or spanbelongs, and/or the like. For example, assume that a span “Susan Wright”or “Susan/NNP, Wright/NNP” is recognized. In such a case, a pattern ofconsecutive NNPs is identified as representing a named entity fallingwithin a “names of persons” category identified as PNAME. As such, thenamed entity feature can include the span “Susan Wright” and the namedentity category (i.e., [Susan_Wright, PNAME]). In some cases, namedentity features can be identified based on POS annotations within akeyword-sentence window or list of tokens. In other cases, name featurescan be identified by referencing a pre-computed list of names.

Parts-of-speech identifiers can also be used to recognize a breakpointfeature. A breakpoint feature, as used herein, refers to an indicationof a point at which a keyword-sentence window, or a sentence therein,can be appropriately truncated. In this regard, truncating a sentence ata breakpoint tends to conform with natural language breaks and avoidsdisrupting readability or comprehension of the snippet. To identifybreakpoints, patterns of part-of-speech identifiers can be recognized.Accordingly, a breakpoint feature can be associated with one or more POSpatterns. If such POS patterns are recognized within a keyword-sentencewindow, or a portion thereof (e.g., a bigram), a breakpoint feature isidentified as such. For example, a comma followed by a conjunction(e.g., “and,” “or,” or “but”) might be recognized as a POS pattern. POSpatterns can be established using any manner, and such patterns are notrequired to only include part-of-speech identifiers. As can beappreciated, a breakpoint feature can include the text associated withthe breakpoint, the part-of-speech pattern, a breakpoint indicator(e.g., BP), and/or a combination thereof. For instance, assume that “,and” is identified in a keyword-sentence window. In such a case, abreakpoint indicator (BP) might be used to designate that such a span isassociated with a breakpoint feature (e.g., [, _and, BP]).

Other text features, such as an address, a phone number, and apredefined name, can be identified independent from part-of-speechidentifiers. A predefined name refers to a predetermined list of tokensand/or sequence of tokens (i.e., span) previously recognized as anentity unacceptable to break. Predefined names can be generated based ona statistical word sequence model that indicates a particular sequenceof words should not be broken. In the case of a predefined name being atoken, a break or snippet boundary following the token is undesirable.In the case of a predefined name being a span, a break or snippetboundary occurring between tokens of the span is undesirable. Forexample, “New York” is an example of a predefined name that is deemedunacceptable to break between the two words of the span.

An address refers to any portion of an address (i.e., a token or span)designating a location. By way of example, and not limitation, city,state, region, country, and/or zipcode, etc. can be recognized asunacceptable to break. A phone number refers to a phone number presentedin any format (e.g., 123.456.7890 or 123-456-7890). In the case of afeature being a token, a break or snippet boundary following the tokenmight be undesirable. In the case of a feature being a span, a break orsnippet boundary occurring between tokens of the span is undesirable.

As with other text features, a predefined name feature, an addressfeature, and/or a phone number feature can include the token or spanand/or an indication that the token or span is a particular type offeature or span (e.g., a predefined name, an address, or a phonenumber). For example, an address span might be “San Francisco, CA” andidentified as an address feature (e.g., [San_Francisco_,_CA, ADDRESS]).As can be appreciated, other text features within a keyword-sentencewindow can be identified and utilized, such as html formatting data(e.g., line breaks, bulleted lists, headings, etc.).

The break-feature identifying component 334 is configured to identifybreak features. A break feature, as used herein, refers to a featurethat indicates an extent to which a break or snippet boundary isacceptable in association with a token and/or span. That is, a breakfeature indicates favorability of snippet boundaries. In embodiments,break features are identified utilizing text features, as more fullydiscussed below. Break features may include, for example, a breakingindicator and a span indicator. A breaking indicator, as used herein,refers to an indication of favorability of breaking a sentence after atoken, before a token, or between two tokens. That is, a breakingindicator indicates whether it is favorable or unfavorable, or an extentto which it is favorable or unfavorable, to break a keyword-sentencewindow at a particular position (e.g., following a particular token). Insome cases, a scale or rating system can be used. For example, abreaking indicator might be a numerical value between −10 and +10. Insuch a case, a negative number might indicate a favorable point tobreak, and a positive number might represent an unfavorable position atwhich to break.

In embodiments, each token of a keyword-sentence window is associatedwith a breaking indicator. In this regard, the breaking indicatorindicates favorability of breaking the keyword-sentence window followingthe token. Alternatively, a breaking indicator can be determined for aportion of the tokens of a keyword-sentence window, such as, tokensassociated with text features, etc. The break feature identifyingcomponent 334 uses the text features of the keyword-sentence window todetermine or identify breaking indicators for tokens of thekeyword-sentence window. For instance, particular bigram types,identification of named entities or categories thereof, addresses, phonenumbers, and predefined names might be used to indicate that it isunfavorable to break following a token or between tokens of a spanassociated with such features. On the other hand, other bigram types andbreakpoints might be used to indicate that it is favorable or acceptableto break the keyword-sentence window following a token or between tokensof a span associated with such features.

A span indicator is used to indicate whether a span is interesting ordroppable. An interesting span indicator provides an indication that aspan is interesting or pertinent to the keyword-sentence window or queryin association therewith. Spans identified as being interesting can beused for various purposes, such as maintaining such spans within asnippet, influencing effectiveness of a particular snippet, etc. A spanindicator might indicate a span as interesting based on text features ofthe keyword-sentence window. For instance, particular bigram types,identification of named entities or categories thereof, addresses, phonenumbers, and predefined names might be identified as interesting andthereby associated with an interesting span indicator (e.g., [“SusanWright”, INTERESTING_SPAN], [“San Francisco, CA”, INTERESTING_SPAN].

A droppable span indicator provides an indication that a span isuninteresting or irrelevant to the keyword-sentence window or queryassociated therewith. Spans identified as being droppable can be usedfor various purposes, such as dropping or removing such spans from asnippet, influencing effectiveness of a particular snippet, or the like.A span indicator might indicate a span as droppable based on textfeatures or a type of text features of the keyword-sentence window. Forexample, particular bigrams types and/or text following a breakpointmight be identified as droppable and thereby associated with a droppablespan indicator (e.g., [“(born 1948)”, DROPPABLE_SPAN].

In some cases, each span within a keyword-sentence window is analyzedand, if appropriate, associated with a span indicator. In other cases, aportion of spans within a keyword-sentence window are assigned a spanindicator. Such portions might be a randomly selected spans, spans forwhich a span indicator is identifiable, etc.

The snippet generator 314 is configured to generate snippets (e.g., suchas partial snippets and optimal snippets). In this regard, when optimalsnippets are displayed in association with a search results, a user canreadily recognize information deemed valuable. In embodiments, thesnippet generator 314 includes a partial-snippet generating component340, a snippet feature determining component 342, a score calculatingcomponent 344, and a snippet selecting component 346.

The partial-snippet generating component 340 is configured to generatepartial snippets from keyword-sentence windows. In this regard, thepartial-snippet generating component 340 generates partial snippetshaving snippet boundaries that truncate the keyword-sentence window atvarious points. As can be appreciated, snippet boundaries might bepositioned at the end of the partial snippet, the beginning of thepartial snippet and/or in the middle or center of the partial snippet. Abeginning snippet boundary refers to a boundary defining the beginningof the partial snippet. An ending snippet boundary refers to a boundarydefining the end of the partial snippet. A center snippet boundaryrefers to a boundary defining at least a part of the middle of thepartial snippet whereby a portion of text is omitted. In other words, acenter snippet boundary coincides with an omission of words (e.g.,identified by an ellipsis). The partial-snippet generating component 340might reference and utilize tokens identified by the tokenizingcomponent 342 to generate partial snippets.

As can be appreciated, partial snippets can be generated in any manner.In one embodiment, an initial partial snippet includes or comprises oneor more keywords within a keyword-sentence window that match a queryterm. By way of example only, assume that a keyword-sentence window is“Susan Wright (born 1948) writes science fiction novels, and lives inSan Francisco, Calif.” In such a case, an initial partial snippet mightinclude the keywords “Wright” and “Incontainables” that match queryterms resulting in the partial snippet of “Wright . . . Incontainables.”The beginning snippet boundary is prior to “Wright,” the ending snippetboundary follows “Incontainables,” a first center snippet boundaryfollows “Wright,” and a second center snippet boundary proceeds“Incontainables.” Although an initial partial snippet is discussedherein as including keywords that match query terms, any text of akeyword-sentence window can be included in an initial partial snippet.

Additional partial snippets can be generated by expanding the initialpartial snippet. Partial snippets can be expanded methodically or in anymanner. In some cases, an initial partial snippet is expanded by addinga token to the left and a token to the right of each token currentlyincluded within a partial snippet. For example, assume that akeyword-sentence window is “Susan Wright (born 1948) writes sciencefiction novels, and lives in San Francisco, Calif.” Further assume thatan initial partial snippet is “Wright . . . Incontainables.” In such acase, a set of expanded partial snippets might be 1) Susan Wright . . .Incontainables, 2) Wright ( . . . Incontainables, 3) Wright . . . andIncontainables, 4) Wright . . . Incontainables., 5) Wright writes . . .Incontainables. As can be appreciated, such expansion can continue usingthe newly generated partial snippets (e.g., until nearly all or all ofthe tokens are included as a partial snippet) to generate additionalsets of expanded partial snippets.

In some embodiments, spans identified as droppable (i.e., having adroppable span indicator) are removed or omitted from a partial snippet.In this regard, partial snippets do not include droppable spans. As canbe appreciated, the partial-snippet generating component 340 cancontemporaneously generate all partial snippets intended to be analyzed.Alternatively, a set of partial snippets might be generated (e.g., aninitial partial snippet, a first set of expanded partial snippets, etc.)and analyzed. Thereafter, the previous partial snippets, or a portionthereof (e.g., a selected partial snippet) can be expanded to generateanother set of partial snippets (e.g., derived from the previous set).

The snippet feature determining component 342 is configured to determinesnippet features. A snippet feature refers to a feature of a partialsnippet. Snippet features can be utilized to identify and/or select anoptimal snippet preferring that potentially relevant information is nottruncated from the keyword-sentence window. A snippet feature might be,for example, a span breakpoint measure, a span measure, a contextmeasure, a merge measure. A span breakpoint measure refers to anindication of favorability of breaking a keyword-sentence window inaccordance with the partial snippet. In this regard, a span breakpointmeasure can indicate a numerical value associated with constructing apartial snippet in accordance with the snippet boundaries of the partialsnippet. A span breakpoint value can be an aggregate or total ofbreaking indicators associated with the breaks of the partial snippet(e.g., breaking indicators identified by break-feature identifyingcomponent 334). By way of example only, assume that a partial snippet is“Susan Wright . . . Incontainables”. Further assume that a breakingindicator after the token “Wright” is (−2), a breaking indicator beforethe token “Incontainables” is (3), a breaking indicator after“Incontainables” is (1), and a breaking indicator before “Susan” is (0).Accordingly, a span breaking indicator is the aggregate of such breakingindicators equal to (2).

A span measure refers to a measure or count of a particular type ofspan. In one embodiment, a span measure is a count of the number ofspans within a partial snippet that are identified as interesting (i.e.,an interesting span identified by break-feature identifying component334). In another embodiment, a span measure is a count of the totalnumber of spans within a partial snippet.

A context measure refers to a number of tokens surrounding a keywordwithin the partial snippet. For example, assume that the partial snippetis “Susan Wright . . . Incontainables” and that the keyword set includeskeywords “Wright” and “Incontainables.” In such a case, the contextmeasure is equal to one in that one token “Susan” surrounds thekeywords. A context measure can be used to recognize instances wheremany tokens surround one keyword, but minimal tokens surround anotherkeyword.

A merge measure refers to a measure indicating whether the partialsnippet has been aggregated or merged with another partial snippet. Ascan be appreciated any number of combination of snippet features can beidentified and/or utilized to calculate a score for a partial snippet,as discussed more fully above. In some cases, a span breakpoint measure,a span measure, a context measure, and/or a merge measure can bedirectly used to calculate a score for a partial snippet. In othercases, such snippet features might be converted, normalized, etc., and,thereafter, utilized to calculate a score for a partial snippet.

The score calculating component 344 is configured to calculate snippetscores for partial snippets. A snippet score indicates the effectivenessor favorability of truncating a keyword-sentence window at snippetboundaries in accordance with a partial snippet. A snippet score cantake on any form including a numerical value, a symbol, text, or thelike. A snippet score can be calculated using any combination,calculation, algorithm, or aggregation of snippet features. The partialsnippets and corresponding scores can be stored for example, in storagedevice 204 of FIG. 2.

The snippet selecting component 346 is configured to select snippets. Inembodiments, the partial snippet with the highest or greatest score isselected. In one embodiment, a partial snippet can be selected from aset of partial snippets to be stored and/or returned to thepartial-snippet generating component 340 for use in generating a set ofexpanded partial snippets derived from the selected partial snippet. Byway of example, assume that a set of partial snippets includes 1) SusanWright . . . Incontainables, 2) Wright ( . . . Incontainables, 3) Wright. . . and Incontainables, 4) Wright . . . Incontainables., 5) Wrightwrites . . . Incontainables. The partial snippet having the highestscore (e.g., “Susan Wright . . . Incontainables [168]”) can be selected,stored, and provided to the partial-snippet generating component 340 forgenerating additional snippets expanding from the selected snippet.Providing a single partial snippet to the partial-snippet generatingcomponent 340, as opposed to each partial snippet, can improve theefficiency of generating partial snippets as fewer variations of partialsnippets are generated. Although discussed herein as returning a singlepartial snippet for expansion, any number of partial snippets can beprovided for generating expanded snippets.

Alternatively or additionally, the snippet selecting component 346 canselect partial snippets as an optimal snippet for display in associationwith search results. An optimal snippet can be selected from among allpartial snippets or from among partial snippets selected from each setof snippets. By way of example, an optimal snippet and correspondingfeatures might be “Susan Wright writes science fiction novels, . . .‘The Green Glass’ and ‘Incontainables.” [BP_Alignment=85, NumHits=2,OptimalContent=90, Merge=0].”

In embodiments, the selected optimal snippet along with thecorresponding features, such as snippet features, text features, and/orbreak features, are provided to a snippet ranker. The snippet rankingcomponent receives references to one or more snippets, along with thefeatures computed in the previous steps, and selects a final snippet forpresentation. These snippets can be the results from the same ordifferent keyword-sentence windows or can be supplied from storage. Thesnippet ranking component assigns overall scores to snippets dependingon features. These features can consist of the scores from the snippetselecting component, as well as additional features that are dependentor independent on the keywords, their position in the document, thequality of the sections from which the keyword-sentence window wasselected and others.

Turning now to FIGS. 4A and 4B, a flow diagram is illustrated whichshows a method 400 for facilitating generation of snippets, inaccordance with an embodiment of the present invention. Initially, atblock 410, a query having one or more query terms is received. Such aquery can be input by a user into a search website. At block 412, arelevant document (i.e., a webpage) having one or more keywords thatmatch one or more query terms is identified. The document is referencedat block 414. At block 416, one or more keyword sets are generated. Suchkeyword sets include combinations of keywords. At block 418, keywordsentences containing one or more keywords are identified. Inembodiments, a keyword sentence might be identified for each keywordwithin the keyword set. Subsequently, at block 420, one or morekeyword-sentence windows are generated. Keyword-sentence windows includea keyword sentence having a keyword that matches a query term and, insome cases, can include other document sentences surrounding the keywordsentence. At block 422, keyword-sentence windows are modified, ifnecessary. In some cases, keyword-sentence windows are modified byaggregating or combining two or more windows due to overlapping of thewindows or to the windows being adjacent to one another.

At block 424, keyword-sentence windows are tokenized to generate a setof tokens, such as words or punctuation. Subsequently, at block 426, apart-of-speech is recognized for each token. The tokens andcorresponding parts-of-speech are analyzed and used to identify any textfeatures including bigram types, named entities, breakpoints, predefinednames, addresses, phone numbers, or the like. This is indicated at block428. In some cases, additional processing might be required to identifysuch text features. For example, to recognize predefined names, thetokens might be compared to a list of predefined names, via a lookupindex table or algorithm. At block 430, break features are identifiedusing the text features. Accordingly, text features are used togenerally identify locations at which it is favorable or unfavorable totruncate a keyword-sentence window. In embodiments, such break featuresinclude break indicators and/or span indicators.

At block 432, a set of one or more partial snippets of thekeyword-sentence window are generated. Snippet features in associationwith each partial snippet are determined, as indicated at block 434. Inembodiments, such snippet features may include a span breakpointmeasure, a span measure, a context measure, a merge measure, or thelike. At block 436, the snippet features are utilized to calculate asnippet score for each snippet. Subsequently, as indicated at block 438,a snippet corresponding with the highest or greatest snippet score fromamong the set of one or more partial snippets is selected. The selectedsnippet is stored at block 440. At block 442, it is determined whetherexpanded partial snippets should be generated. If it is determined thatexpanded partial snippets should be generated, the selected snippet isused to generate another set of one or more partial snippets that areexpanded from the selected snippet, as indicated at block 432. Forexample, the selected snippet can be expanded by adding a token to theleft and right of each token or span of the existing selected snippet.If, however, it is determined that expanded partial snippets are notdesired, the best candidate of partial snippets is selected. This isindicated at block 444. Accordingly, at block 444, an optimal snippet isselected. Such an optimal snippet selection might be based on the scorescalculated for each partial snippet.

It will be understood by those of ordinary skill in the art that theorder of steps shown in the method 400 of FIGS. 4A and 4B are not meantto limit the scope of the present invention in any way and, in fact, thesteps may occur in a variety of different sequences within embodimentshereof. Any and all such variations, and any combination thereof, arecontemplated to be within the scope of embodiments of the presentinvention.

The present invention has been described in relation to particularembodiments, which are intended in all respects to be illustrativerather than restrictive. Alternative embodiments will become apparent tothose of ordinary skill in the art to which the present inventionpertains without departing from its scope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated by and is within the scope ofthe claims.

1. One or more computer storage media having computer-executableinstructions embodied thereon, that when executed, cause a computingdevice to perform a method for facilitating generation of snippetsprovided in association with search results, the method comprising:referencing a keyword-sentence window comprising a sequence of tokensincluding one or more keywords that match one or more query terms;identifying a part-of-speech for one or more tokens within thekeyword-sentence window; and utilizing the part-of-speech correspondingwith each of the one or more tokens to identify one or more textfeatures associated with a span including two or more tokens, the one ormore text features being used to generate a snippet comprising a portionof the keyword-sentence window truncated at optimal snippet boundaries.2. The media of claim 1 further comprising displaying the snippet inassociation with a search result.
 3. The media of claim 1, wherein thesnippet comprising the portion of the keyword-sentence window truncatedat optimal snippet boundaries provides text that breaks at naturalbeginning and ending points.
 4. The media of claim 1, wherein thesnippet effectively characterizes a document in association with asearch result such that potentially relevant information is nottruncated from the keyword-sentence window.
 5. The media of claim 1,wherein the one or more text features comprise a bigram type, a namedentity, a breakpoint, or a combination thereof.
 6. The media of claim 5,wherein the bigram type comprises an indication of a part-of-speechsequence corresponding with a bigram.
 7. The media of claim 5, whereinthe named entity is identified based on a pattern of part-of-speechidentifiers.
 8. The media of claim 5, wherein the breakpoint comprisesan indication of a point at which the keyword-sentence window can befavorably truncated.
 9. The media of claim 1 further comprisingidentifying one or more additional text features based on tokens orspans within the keyword-sentence window.
 10. The media of claim 9further comprising utilizing the one or more text features and the oneor more additional text features to determine one or more break featuresthat indicate favorability of breaking the keyword-sentence window. 11.The media of claim 10, wherein the one or more break features comprise abreaking indicator, a span indicator, or a combination thereof.
 12. Themethod of claim 11, wherein the one or more break features are used togenerate the snippet comprising the portion of the keyword-sentencewindow truncated at the optimal snippet boundaries.
 13. A method forfacilitating generation of snippets provided in association with searchresults, the method comprising: identifying one or more text featuresfor a plurality of spans within a keyword-sentence window, at least aportion of the text features being identified based on a part-of-speechidentifier associated with one or more tokens of the span; determiningone or more break features associated with one or more of the pluralityof spans using the one or more text features, the one or more breakfeatures providing an indication of whether a snippet boundary isfavorable relative to a particular position within the keyword-sentencewindow; and utilizing the one or more break features to generate asnippet comprising a portion of the keyword-sentence window truncated atappropriate snippet boundaries.
 14. The method of claim 13, wherein theone or more break features comprise a breaking indicator to indicatefavorability of breaking a sentence and a span indicator to indicatewhether a span is interesting or droppable.
 15. The method of claim 14further comprising using the span indicator and the breaking indicatorto determine snippet features that indicate features of the snippet. 16.The method of claim 15 further comprising using the snippet features tocalculate a snippet score for the snippet that indicates favorability oftruncating the keyword-sentence window in accordance with the snippetboundaries.
 17. The method of claim 13 further comprising displaying thesnippet in association with a search result.
 18. One or more computerstorage media having computer-executable instructions embodied thereon,that when executed, cause a computing device to perform a method forfacilitating generation of snippets provided in association with searchresults, the method comprising: identifying one or more text featuresassociated with spans within a keyword-sentence window including atleast one keyword that matches at least one query term, at least aportion of the one or more text features being identified based on apart-of-speech identifier associated with one or more tokens of thespan; determining one or more break features associated with the spansusing the one or more text features, the one or more break featuresproviding an indication of whether a snippet boundary is favorablerelative to a particular position within the keyword-sentence window;generating a plurality of partial snippets comprising portions of thekeyword-sentence window; for each partial snippet, identifying one ormore snippet features that indicate a relative strength of truncatingthe keyword-sentence window in accordance with the partial snippet;determining a score for each of the plurality of partial snippets thatindicates favorability of truncating the keyword-sentence window atsnippet boundaries as indicated in the partial snippet, the score beingbased on the one or more snippet features; and based on the scores,selecting a partial snippet from the plurality of partial snippets todisplay in association with a search result, the selected partialsnippet designated as having optimal snippet boundaries.
 19. The mediaof claim 18 further comprising displaying the selected partial snippetin association with a search result.
 20. The media of claim 18, whereingenerating the plurality of partial snippets comprises expanding one ormore previous partial snippets.